Social networks are increasingly becoming a means for interacting with one another to disseminate and discover useful content. In popular social networking sites such as Facebook and LinkedIn, users share updates of their activities within their social circle of contacts. The updates typically include recently uploaded photos, comments on photos and news articles, reviews and ratings that the user has assigned to a movie or restaurant, or simply an article or game on the web that the user has liked. Each contact further recursively shares received updates within its own social circle of contacts, and thereby content generated by a user propagates through the network to a wide user population. Thus, social networks enable users to share content at an unprecedented scale, and discover new content of interest to them.
The extent to which a social network spreads content is a key metric that impacts both user engagement and network revenues. As the content spreading increases, the more novel content users end up discovering, and the more value users derive from being part of the social network. This helps to drive up user engagement which in turn leads to improved user retention and audience growth through word-of-mouth recruitment. Furthermore, as users spend more time accessing diverse content in the form of photos, news articles, games etc., there are increased opportunities for monetizing the content via online ads, sale of virtual goods, subscriptions, and so on. Additionally, new “social” ad formats have features that enable sharing, and so a single ad impression can be viewed by thousands of users in the social network. Also, social ads command a much higher price per impression compared to normal online ads depending on how widely they are distributed in the social network. Thus, they can provide significant revenue lifts. Due to such benefits, it is therefore crucial for social networks to maximize the dissemination of interesting content across the entire social graph.
The degree to which content is disseminated within the network depends on the connectivity relationships among network users. Typically, social networking sites like Facebook and LinkedIn already offer “people recommendations” to each user to increase connectivity among the users. The sites recommend a set of people that the user may want to connect with. However, current people recommender implementations on social networking sites are not geared towards increasing content availability. For instance, the “People You May Know” feature on Facebook employs the Friend-of-Friend (FoF) algorithm that recommends friends of a friend with the rationale that a user is very likely to know close friends of his or her friends. Specifically, FoF recommends users in decreasing order of the number of common friends with the user receiving the recommendation.
Existing schemes for recommending connections in social networks are based on the number of common contacts, similarity of user profiles, etc. For example, existing schemes for recommending connections suggest users whose profiles, interests, or updates have substantial overlap with the receiver of the recommendation. However, simply forming connections based on the number of mutual friends or similarity between profiles or posted content may not maximize the amount of content spread in the social network.
Based on the foregoing, there is a need for a method and system for spreading content in the social network and to overcome the abovementioned shortcoming in the field of the present invention.